code: def var_param_shift(
      tape,
      argnum,
      shifts=None,
      gradient_recipes=None,
      f0=None,
      broadcast=False,
  ):
      r"""Generate the parameter-shift tapes and postprocessing methods required
      to compute the gradient of a gate parameter with respect to a
      variance value.
  
      The post-processing function returns a tuple instead of stacking results.
  
      Args:
          tape (.QuantumTape): quantum tape to differentiate
          argnum (int or list[int] or None): Trainable parameter indices to differentiate
              with respect to. If not provided, the derivative with respect to all
              trainable indices are returned.
          shifts (list[tuple[int or float]]): List containing tuples of shift values.
              If provided, one tuple of shifts should be given per trainable parameter
              and the tuple should match the number of frequencies for that parameter.
              If unspecified, equidistant shifts are assumed.
          gradient_recipes (tuple(list[list[float]] or None)): List of gradient recipes
              for the parameter-shift method. One gradient recipe must be provided
              per trainable parameter.
          f0 (tensor_like[float] or None): Output of the evaluated input tape. If provided,
              and the gradient recipe contains an unshifted term, this value is used,
              saving a quantum evaluation.
          broadcast (bool): Whether or not to use parameter broadcasting to create the
              a single broadcasted tape per operation instead of one tape per shift angle.
  
      Returns:
          tuple[list[QuantumTape], function]: A tuple containing a
          list of generated tapes, together with a post-processing
          function to be applied to the results of the evaluated tapes
          in order to obtain the Jacobian matrix.
      """
  
      argnum = argnum or tape.trainable_params
  
      # Determine the locations of any variance measurements in the measurement queue.
      var_mask = [isinstance(m, VarianceMP) for m in tape.measurements]
      var_indices = np.where(var_mask)[0]
  
      # Get <A>, the expectation value of the tape with unshifted parameters.
      expval_tape = tape.copy(copy_operations=True)
  
      # Convert all variance measurements on the tape into expectation values
      for i in var_indices:
          obs = expval_tape.measurements[i].obs
          expval_tape._measurements[i] = qml.expval(op=obs)
          if obs.name == "Hamiltonian":
              first_obs_idx = len(expval_tape.operations)
              for t_idx in reversed(
                  range(len(expval_tape.trainable_params))
              ):
                  op, op_idx, _ = expval_tape.get_operation(t_idx)
                  if op_idx < first_obs_idx:
                      break  # already seen all observables
                  if op is obs:
                      raise ValueError(
                          "Can only differentiate Hamiltonian coefficients for expectations, not variances"
                      )
  
      # evaluate the analytic derivative of <A>
      pdA_tapes, pdA_fn = expval_param_shift(
          expval_tape, argnum, shifts, gradient_recipes, f0, broadcast
      )
      gradient_tapes = (
          [] if pdA_fn.first_result_unshifted else [expval_tape]
      )
      gradient_tapes.extend(pdA_tapes)
  
      # Store the number of first derivative tapes, so that we know
      # the number of results to post-process later.
      tape_boundary = len(gradient_tapes)
  
      # If there are non-involutory observables A present, we must compute d<A^2>/dp.
      # Get the indices in the measurement queue of all non-involutory
      # observables.
      non_involutory_indices = []
  
      for i in var_indices:
          obs_name = tape.observables[i].name
  
          if isinstance(obs_name, list):
              # Observable is a tensor product, we must investigate all constituent observables.
              if any(name in NONINVOLUTORY_OBS for name in obs_name):
                  non_involutory_indices.append(i)
  
          elif obs_name in NONINVOLUTORY_OBS:
              non_involutory_indices.append(i)
  
      pdA2_fn = None
      if non_involutory_indices:
          tape_with_obs_squared_expval = tape.copy(copy_operations=True)
  
          for i in non_involutory_indices:
              # We need to calculate d<A^2>/dp; to do so, we replace the
              # involutory observables A in the queue with A^2.
              obs = _square_observable(
                  tape_with_obs_squared_expval.measurements[i].obs
              )
              tape_with_obs_squared_expval._measurements[
                  i
              ] = qml.expval(op=obs)
  
          # Non-involutory observables are present; the partial derivative of <A^2>
          # may be non-zero. Here, we calculate the analytic derivatives of the <A^2>
          # observables.
          pdA2_tapes, pdA2_fn = expval_param_shift(
              tape_with_obs_squared_expval,
              argnum,
              shifts,
              gradient_recipes,
              f0,
              broadcast,
          )
          gradient_tapes.extend(pdA2_tapes)
  
      processing_fn = _create_variance_proc_fn(
          tape,
          var_mask,
          var_indices,
          pdA_fn,
          pdA2_fn,
          tape_boundary,
          non_involutory_indices,
      )
      return gradient_tapes, processing_fn
  
imports: from typing import Sequence, Callable
  from functools import partial
  import numpy
  import pennylane
  from pennylane.measurements import VarianceMP
  from pennylane import transform
  from pennylane.transforms.tape_expand import expand_invalid_trainable
  from pennylane.gradients.gradient_transform import _contract_qjac_with_cjac
  from .finite_difference import finite_diff
  from .general_shift_rules import _iterate_shift_rule, frequencies_to_period, generate_shifted_tapes, process_shifts
  from .gradient_transform import _all_zero_grad, assert_no_state_returns, assert_no_tape_batching, assert_multimeasure_not_broadcasted, choose_trainable_params, find_and_validate_gradient_methods, _no_trainable_grad, reorder_grads
label_desc: Use image node_id 16 for calling a global function with example usage: var_param_shift(tape, argnum, shifts, gradient_recipes, f0, broadcast) and returns: gradient_tapes,  processing_fn